Kullback-Leibler Divergence Based Stealthy Deception Attacks Against Multi-Sensor Remote State Estimation Under Limited Resources

Haibin Guo; Lin Li; Zhong-Hua Pang; Honggui Han

doi:10.1109/JAS.2025.125954

Volume 13 Issue 9

Sep. 2026

IEEE/CAA Journal of Automatica Sinica

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Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2026 > 13(9): 2039-2048

H. Guo, L. Li, Z.-H. Pang, and H. Han, “Kullback-Leibler divergence based stealthy deception attacks against multi-sensor remote state estimation under limited resources,” IEEE/CAA J. Autom. Sinica, vol. 13, no. 9, pp. 2039–2048, Sep. 2026. doi: 10.1109/JAS.2025.125954

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H. Guo, L. Li, Z.-H. Pang, and H. Han, “Kullback-Leibler divergence based stealthy deception attacks against multi-sensor remote state estimation under limited resources,” IEEE/CAA J. Autom. Sinica, vol. 13, no. 9, pp. 2039–2048, Sep. 2026. doi: 10.1109/JAS.2025.125954

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Kullback-Leibler Divergence Based Stealthy Deception Attacks Against Multi-Sensor Remote State Estimation Under Limited Resources

doi: 10.1109/JAS.2025.125954

Funds: This work was supported in part by the National Natural Science Foundation of China (62403010, 62673012, 62573005, 52301408); the China Postdoctoral Science Foundation (2025T180466, 2024M750192); the Beijing Postdoctoral Research Foundation (2025-ZZ-70); the Fund Project of Intelligent Perception and Control Key Laboratory of Sichuan Province (2024RYY05); and Beijing Natural Science Foundation (L241015)

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Author Bio:
Haibin Guo (Member, IEEE) received the B.Sc. degree in applied physics from Shandong University of Science and Technology in 2017, and the Ph.D. degree in control science and engineering from the School of Automation, Beijing Institute of Technology in 2023. He was a Postdoctoral Research Fellow at North China University of Technology from Oct. 2023 to Jan. 2026. Since 2026, he has been with North China University of Technology, where he is currently an Associate Professor. His research interests include networked control systems, attack modeling and detection, and security of cyber-physical systems

Lin Li received the B.Eng. degree in control theory and control engineering from University of Science and Technology Beijing in 2009. She is currently a Lecture with Beijing Union University and is working toward the Ph.D. degree in control science and engineering with the North China University of Technology. Her current research interests include multiagent systems and networked control systems

Zhong-Hua Pang (Senior Member, IEEE) received the B.Eng. degree in automation and M.Eng. degree in control theory and control engineering from Qingdao University of Science and Technology in 2002 and 2005, respectively, and the Ph.D. degree in control theory and control engineering from the Institute of Automation, Chinese Academy of Sciences in 2011. He was a Postdoctoral Research Fellow with Tsinghua University from Sep. 2011 to Feb. 2014, a Visiting Scholar at the University of South Wales, UK from Nov. 2016 to Oct. 2017, and a Visiting Scholar at the Swinburne University of Technology, Australia from Jul. 2019 to Aug. 2019 and from Jan. 2020 to Mar. 2020, respectively. Since 2014, he has been with North China University of Technology, first as an Associate Professor and since 2017 as a Professor. His current research interests include networked control systems, multi-agent systems, security of cyber-physical systems, and data-driven control systems

Honggui Han (Senior Member, IEEE) received the B.S. degree in automation from the Civil Aviation University of China in 2005, the M.E. and Ph.D. degrees in control theory and control engineering from Beijing University of Technology in 2007 and 2011, respectively. Since 2011, he has been with Beijing University of Technology, where he is currently a Professor. His current research interests include neural networks, fuzzy systems, intelligent systems, modeling and control in process systems, and civil and environmental engineering
Corresponding author: Zhong-Hua Pang, e-mail: zhonghua.pang@ia.ac.cn
Received Date: 2025-06-24
Revised Date: 2025-08-24
Accepted Date: 2025-10-03

Abstract

Abstract

Cyber-physical systems enable the remote monitoring and control of physical plants by seamlessly integrating computation, communication, and control. Unfortunately, security implications posed by deception attacks have become increasingly prominent. Designing deception attacks is fundamental to analyzing system vulnerabilities, yet their impact is constrained by attack stealthiness and limited attack resources. Thus, an ε-stealthy deception attack scheme against multi-sensor remote state estimation is proposed in this paper, where only partial sensor residuals are manipulated under limited attack resources. Kullback-Leibler divergence is used to quantify the ε-stealthiness, and a constraint on the covariance of the compromised residual is established. The estimation error covariance of the compromised system is then derived as the attacked objective. Next, the worst-case covariance of the compromised residual is determined by maximizing the trace of the system estimation error covariance under the residual covariance constraint. Based on this, a constraint optimization problem is formulated to derive a worst-case attack strategy. Finally, simulation results are provided to validate the effectiveness of the proposed attack scheme.
- Attack stealthiness,
- cyber-physical systems (CPSs),
- deception attacks,
- remote state estimation

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References(39)

References

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Kullback-Leibler Divergence Based Stealthy Deception Attacks Against Multi-Sensor Remote State Estimation Under Limited Resources

doi: 10.1109/JAS.2025.125954

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